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  • 1.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Blomqvist, Eva
    RISE SICS East, Linköping, Sweden.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-based Context-aware System for Smart Homes: E-care@home2017In: Sensors, E-ISSN 1424-8220, Vol. 17, no 7, article id 1586Article in journal (Refereed)
    Abstract [en]

    Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

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  • 2.
    Köckemann, Uwe
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Tsiftes, Nicolas
    RISE SICS, RISE Research Institutes of Sweden, Stockholm, Sweden.
    Ahmed, Mobyen Uddin
    School of Innovation Design and Engineering (IDT), Mälardalen University, Västerås, Sweden.
    Morberg, Daniel
    School of Innovation Design and Engineering (IDT), Mälardalen University, Västerås, Sweden.
    Lindén, Maria
    School of Innovation Design and Engineering (IDT), Mälardalen University, Västerås, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 3, article id E879Article in journal (Refereed)
    Abstract [en]

    As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

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    Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes
  • 3.
    Lunardini, Francesca
    et al.
    Politecnico Di Milano, Milan, Italy.
    Luperto, Matteo
    University of Milan, Milan, Italy.
    Romeo, Marta
    University of Manchester, Manchester, UK.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Basilico, Nicola
    University of Milan, Milan, Italy.
    Krpič, Andrej
    Smart Com, Ljubljana, Slovenia.
    Borghese, Nunzio Alberto
    University of Milan, Milan, Italy.
    Ferrante, Simona
    Politecnico di Milano, Milan, Italy.
    The MOVECARE project: Home-based monitoring of frailty2019In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI): Proceedings, IEEE, 2019, article id 8834482Conference paper (Refereed)
    Abstract [en]

    Concerning frailty, the use of home-based technology able to continuously and transparently monitor the independent elder may represent a useful tool to support the traditional geriatric assessment in the identification of elderly people at risk of frailty, with the final aim of guiding the development of early preventive interventions. To this aim, the European MoveCare project develops an ICT platform to support the independent living of the elder at home. Here, we describe how home-based monitoring of frailty is addressed within MoveCare, specifically for the five Fried criteria. The platform leverages a net of heterogeneous sensors a service robot, and the use of gamification to achieve ecological monitoring of frailty through quantitative measurements transparently recorded during common daily-life activities. The indicators collected over time are fed to the reasoning entity of the platform to provide informal caregivers with relevant information on the elder's status.

  • 4.
    Luperto, Matteo
    et al.
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Italy.
    Monroy, Javier
    Machine Perception and Intelligent Robotics Group, Department of System Engineering and Automation, Biomedical Research Institute of Malaga, University of Malaga, Spain.
    Moreno, Francisco-Angel
    Machine Perception and Intelligent Robotics Group, Department of System Engineering and Automation, Biomedical Research Institute of Malaga, University of Malaga, Spain.
    Lunardini, Francesca
    NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Krpic, Andrej
    Smart-Com, Ljubljana, Slovenia.
    Galindo, Cipriano
    Machine Perception and Intelligent Robotics Group, Department of System Engineering and Automation, Biomedical Research Institute of Malaga, University of Malaga, Spain.
    Ferrante, Simona
    NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy.
    Basilico, Nicola
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Italy.
    Gonzalez-Jimenez, Javier
    Machine Perception and Intelligent Robotics Group, Department of System Engineering and Automation, Biomedical Research Institute of Malaga, University of Malaga, Spain.
    Borghese, N. Alberto
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Italy.
    Seeking at-home long-term autonomy of assistive mobile robots through the integration with an IoT-based monitoring system2023In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 161, article id 104346Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a system that stems from the integration of an autonomous mobile robot with an IoT-based monitoring system to provide monitoring, assistance, and stimulation to older adults living alone in their own houses. The creation of an Internet of Robotics Things (IoRT) based on the interplay between pervasive smart objects and autonomous robotic systems is claimed to enable the creation of innovative services conceived for assisting the final user, especially in elderly care. The synergy between IoT and a Socially Assistive Robot (SAR) was conceived to offer robustness, reconfiguration, heterogeneity, and scalability, by bringing a strong added value to both the current SAR and IoT technologies. First, we propose a method to achieve the synergy and integration between the IoT system and the robot; then, we show how our method increases the performance and effectiveness of both to provide long-term support to the older adults. To do so, we present a case-study, where we focus on the detection of signs of the frailty syndrome, a set of vulnerabilities typically conveyed by a cognitive and physical decline in older people that concur in amplifying the risks of major diseases hindering the capabilities of independent living. Experimental evaluation is performed in both controlled settings and in a long-term real-world pilot study with 9 older adults in their own apartments, where the system was deployed autonomously for, on average, 12 weeks.

  • 5.
    Luperto, Matteo
    et al.
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    Monroy, Javier
    Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, University of Malaga, and Instituto de Investigación Biomédica de Málaga-IBIMA, Malaga, Spain.
    Renoux, Jennifer
    Örebro University, School of Science and Technology. Machine Perception and Interaction Lab.
    Lunardini, Francesca
    NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
    Basilico, Nicola
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    Bulgheroni, Maria
    AB.Acus SRL, Milan, Italy.
    Cangelosi, Angelo
    University of Manchester, Manchester, UK.
    Cesari, Matteo
    IRCCS Istituti Clinici Scientifici Maugeri, University of Milan, Milan, Italy.
    Cid, Manuel
    Junta de Extremadura, Merida, Spain.
    Ianes, Aladar
    Korian, Milan, Italy.
    Gonzalez-Jimenez, Javier
    Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, University of Malaga, and Instituto de Investigación Biomédica de Málaga-IBIMA, Malaga, Spain.
    Kounoudes, Anastasis
    SignalGeneriX, Limassol, Cyprus.
    Mari, David
    EURECAT, Barcelona, Spain.
    Prisacariu, Victor
    University of Oxford, Oxford, UK.
    Savanovic, Arso
    Smart Com, Ljubjana, Slovenia.
    Ferrante, Simona
    NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
    Borghese, N. Alberto
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project2023In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 15, no 3, p. 517-545Article in journal (Refereed)
    Abstract [en]

    The integration of Ambient Assisted Living (AAL) frameworks with Socially Assistive Robots (SARs) has proven useful for monitoring and assisting older adults in their own home. However, the difficulties associated with long-term deployments in real-world complex environments are still highly under-explored. In this work, we first present the MoveCare system, an unobtrusive platform that, through the integration of a SAR into an AAL framework, aimed to monitor, assist and provide social, cognitive, and physical stimulation in the own houses of elders living alone and at risk of falling into frailty. We then focus on the evaluation and analysis of a long-term pilot campaign of more than 300 weeks of usages. We evaluated the system's acceptability and feasibility through various questionnaires and empirically assessed the impact of the presence of an assistive robot by deploying the system with and without it. Our results provide strong empirical evidence that Socially Assistive Robots integrated with monitoring and stimulation platforms can be successfully used for long-term support to older adults. We describe how the robot's presence significantly incentivised the use of the system, but slightly lowered the system's overall acceptability. Finally, we emphasise that real-world long-term deployment of SARs introduces a significant technical, organisational, and logistical overhead that should not be neglected nor underestimated in the pursuit of long-term robust systems. We hope that the findings and lessons learned from our work can bring value towards future long-term real-world and widespread use of SARs.

  • 6.
    Luperto, Matteo
    et al.
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    Romeo, Marta
    University of Manchester, Manchester, UK.
    Monroy, Javier
    Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, University of Malaga, Spain; Instituto de Investigación Biomédica de Málaga–IBIMA BIONAND Platform, University of Málaga, Spain.
    Renoux, Jennifer
    Örebro University, School of Science and Technology. Machine Perception and Interaction Lab.
    Vuono, Alessandro
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    Moreno, Francisco-Angel
    Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, University of Malaga, Spain; Instituto de Investigación Biomédica de Málaga–IBIMA BIONAND Platform, University of Málaga, Spain.
    Gonzalez-Jimenez, Javier
    Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, University of Malaga, Spain; Instituto de Investigación Biomédica de Málaga–IBIMA BIONAND Platform, University of Málaga, Spain.
    Basilico, Nicola
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    Borghese, N. Alberto
    Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
    User feedback and remote supervision for assisted living with mobile robots: A field study in long-term autonomy2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104170Article in journal (Refereed)
    Abstract [en]

    In an ageing society, the at-home use of Socially Assistive Robots (SARs) could provide remote monitoring of their users' well-being, together with physical and psychological support. However, private home environments are particularly challenging for SARs, due to their unstructured and dynamic nature which often contributes to robots' failures. For this reason, even though several prototypes of SARs for elderly care have been developed, their commercialisation and wide-spread at-home use are yet to be effective. In this paper, we analyse how including the end users' feedback impacts the SARs reliability and acceptance. To do so, we introduce a Monitoring and Logging System (MLS) for remote supervision, which increases the explainability of SAR-based systems deployed in older adults' apartments, while also allowing the exchange of feedback between caregivers, technicians, and older adults. We then present an extensive field study showing how long-term deployment of autonomous SARs can be accomplished by relying on such a feedback loop to address any potential issue. To this end, we provide the results obtained in a 130-week long study where autonomous SARs were deployed in the apartments of 10 older adults, with the aim of possibly serving and assisting future practitioners, with the knowledge collected from this extensive experimental campaign, to fill the gap that currently exists for the widespread adoption of SARs.

  • 7.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Active Situation Reporting: Definition and Analysis2017Conference paper (Refereed)
  • 8. Renoux, Jennifer
    Contribution to multiagent planning for active information gathering2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In this thesis, we address the problem of performing event exploration. We define event exploration as the process of exploring a topologically known environment to gather information about dynamic events in this environment. Multiagent systems are commonly used for information gathering applications, but bring important challenges such as coordination and communication. This thesis proposes a new fully decentralized model of multiagent planning for information gathering. In this model, called MAPING (Multi-Agent Planning for INformation Gathering ), the agents use an extended belief state that contains not only their own beliefs but also approximations of other agents’ beliefs. With this extended belief state they are able to quantify the relevance of a piece of information for themselves but also for others. They can then decide to explore a specific area or to communicate a specific piece of information according to the action that brings the most information to the system in its totality. The major drawback of this model is its complexity: the size of the belief states space increases exponentially with the number of agents and the size of the environment. To overcome this issue, we also suggest a solving algorithm that uses the well-known adopted assumption of variable independence.

    Finally we consider the fact that event exploration is usually an open-ended problem. Therefore the agents need to check again their beliefs even after they reached a good belief state. We suggest a smoothing function that enables the agents to forget gradually old observations that can be obsolete.

    We evaluated our model on different scenarios inspired by real-type applications. These experiments show the ability of MAPING to tackle the event exploration problem with limited communications.

  • 9.
    Renoux, Jennifer
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors2017In: Knowledge-based techniques for problem solving and reasoning(KnowProS 2017): A workshop at AAAI 2017, February 5, 2017, San Francisco, U.S.A., Palo Alto: AAAI Press, 2017, Vol. ws17, p. 758-764, article id WS-17-12Conference paper (Refereed)
    Abstract [en]

    Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

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  • 10.
    Renoux, Jennifer
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Simulating Daily Activities in a Smart Home for Data Generation2018In: 2018 Winter Simulation Conference (WSC), New York, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 798-809Conference paper (Refereed)
    Abstract [en]

    Smart Homes are currently one of the hottest topics in the area of Internet of Things or Augmented Living. In order to provide high-level intelligent solutions, algorithms for identifying which activities the inhabitants intend to perform are necessary. Sensor data plays here an essential role, for testing, for learning underlying rules, for classifying and connecting sensor patterns and to inhabitant activities, etc. However, only few and limited data sets are currently available. We present concepts and solutions for generating high-quality data using a flexible agent-based simulation tool. The basic idea is to integrate the simulation of a sensorized apartment with human behavior modelling based on constraint-based planning that produces a sequence of daily activities. The overall set-up is shown to generate data that exhibits the same relevant properties as data from a comparable real-world apartment.

  • 11.
    Renoux, Jennifer
    et al.
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning2018In: Ambient Intelligence / [ed] Achilles Kameas, Kostas Stathis, Springer, 2018, Vol. 11249, p. 74-89Conference paper (Refereed)
    Abstract [en]

    Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system’s ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.

  • 12.
    Renoux, Jennifer
    et al.
    Örebro University, School of Science and Technology.
    Luperto, Matteo
    Applied Intelligent Systems Lab, University of Milan, Milan, Italy.
    Basilico, Nicola
    Applied Intelligent Systems Lab, University of Milan, Milan, Italy.
    Romeo, Marta
    University of Manchester, Manchester, United Kingdom.
    Milis, Marios
    SignalGeneriX Ltd, Lemesos, Cyprus.
    Lunardini, Francesca
    NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
    Ferrante, Simona
    NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Borghese, N. Alberto
    Applied Intelligent Systems Lab, University of Milan, Milan, Italy.
    A Virtual Caregiver for Assisted Daily Living of Pre-Frail Users2020In: KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020, Proceedings / [ed] Ute Schmid, Franziska Klügl, Diedrich Wolter, Cham: Springer, 2020, p. 176-189Conference paper (Refereed)
    Abstract [en]

    As Europe sees its population aging dramatically, Assisted Daily Living for the elderly becomes a more and more important and relevant research topic. The Movecare Project focuses on this topic by integrating a robotic platform, an IoT system, and an activity center to provide assistance, suggestions of activities and transparent monitoring to users at home. In this paper, we describe the Virtual Caregiver, a software component of the Movecare platform, that is responsible for analyzing the data from the various modules and generating suggestions tailored to the user’s state and needs. A preliminary study has been carried on over 2 months with 15 users. This study suggests that the presence of the Virtual Caregiver encourages people to use the Movecare platform more consistently, which in turn could result in better monitoring and prevention of cognitive and physical decline.

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    A Virtual Caregiver for Assisted Daily Living of Pre-frail Users
  • 13.
    Renoux, Jennifer
    et al.
    GREYC Laboratory, Université de Caen, Caen, France.
    Mouaddib, Abdel-Illah
    GREYC Laboratory, Université de Caen, Caen, France.
    Gloannec, Simon Le
    Airbus Defence and Space, Val de Reuil, France.
    A decision-theoretic planning approach for multi-robot exploration and event search2015In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper (Refereed)
    Abstract [en]

    Event exploration is the process of exploring a topologically known environment to gather information about dynamic events in this environment. Using multi-robot systems for event exploration brings major challenges such as finding and communicating relevant information. This paper presents a solution to these challenges in the form of a distributed decision-theoretic model called MAPING (Multi-Agent Planning for INformation Gathering), in which each agent computes a communication and an exploration strategy by assessing the relevance of an observation for another agent. The agents use an extended belief state that contains not only their own beliefs but also approximations of other agents’ beliefs. MAPING includes a forgetting mechanism to ensure that the event-exploration remains open-ended. To overcome the resolution complexity due to the extended belief state we use a method based on the well-known adopted assumption of variables independence. We evaluate our approach on different event exploration problems with varying complexity. The experimental results on simulation show the effectiveness of MAPING, its ability to scale up and its ability to face real-word applications.

  • 14.
    Renoux, Jennifer
    et al.
    University of Caen, Caen, France.
    Mouaddib, Abdel-Illah
    University of Caen, Caen, France.
    LeGloannec, Simon
    Airbus Defence and Space, Val de Reuil, France.
    Distributed Decision-Theoretic Active Perception for Multi-robot Active Information Gathering2014In: Modeling Decisions for Artificial Intelligence: 11th International Conference, MDAI 2014, Tokyo, Japan, October 29-31, 2014: Proceedings / [ed] Torra, V.; Narukawa, Y., Endo, Y., Springer , 2014, Vol. 8825, p. 60-71Conference paper (Refereed)
    Abstract [en]

    Multirobot systems have made tremendous progress in exploration and surveillance. In that kind of problem, agents are not required to perform a given task but should gather as much information as possible. However, information gathering tasks usually remain passive. In this paper, we present a multirobot model for active information gathering. In this model, robots explore, assess the relevance, update their beliefs and communicate the appropriate information to relevant robots. To do so, we propose a distributed decision process where a robot maintains a belief matrix representing its beliefs and beliefs about the beliefs of the other robots. This decision process uses entropy and Kullback-Leibler in a reward function to access the relevance of their beliefs and the divergence with each other. This model allows the derivation of a policy for gathering information to make the entropy low and a communication policy to reduce the divergence. An experimental scenario has been developed for an indoor information gathering mission.

  • 15.
    Renoux, Jennifer
    et al.
    Airbus Defence and Space,Toulouse, France.
    Mouaddib, Abdel-Illah
    Université de Caen, Caen, France.
    LeGloannec, Simon
    Airbus Defence and Space,Toulouse, France.
    Method For Obtaining A System For Active, Decentralized Multi-agent Situation Control2015Patent (Other (popular science, discussion, etc.))
  • 16.
    Renoux, Jennifer
    et al.
    Örebro University, School of Science and Technology.
    Veiga, Tiago S.
    Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway; Institute for Systems and Robotics, Instituto Superior Tecnico, University of Lisbon, Portugal.
    Lima, Pedro U.
    Institute for Systems and Robotics, Instituto Superior Tecnico, University of Lisbon, Portugal.
    Spaan, Matthijs T. J.
    Department of Software Technology, Delft University of Technology, The Netherlands.
    A Unified Decision-Theoretic Model for Information Gathering and Communication Planning2020In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2020, p. 67-74Conference paper (Refereed)
    Abstract [en]

    We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human's beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.

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    A Unified Decision-Theoretic Model for Information Gathering and Communication Planning
  • 17.
    Veiga, Tiago
    et al.
    Department of Computer Science, Norwegian University of Science and Technology, Norway.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    From Reactive to Active Sensing: A Survey on Information Gathering in Decision-theoretic Planning2023In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 55, no 13S, article id 280Article in journal (Refereed)
    Abstract [en]

    In traditional decision-theoretic planning, information gathering is a means to a goal. The agent receives information about its environment (state or observation) and uses it as a way to optimize a state-based reward function. Recent works, however, have focused on application domains in which information gathering is not only the mean but the goal itself. The agent must optimize its knowledge of the environment. However, traditional Markov-based decision-theoretic models cannot account for rewarding the agent based on its knowledge, which leads to the development of many approaches to overcome this limitation. We survey recent approaches for using decision-theoretic models in information-gathering scenarios, highlighting common practices and existing generic models, and show that existing methods can be categorized into three classes: reactive sensing, single-agent active sensing, and multi-agent active sensing. Finally, we highlight potential research gaps and suggest directions for future research.

  • 18.
    Vuono, Alessandro
    et al.
    University of Milan, Milan, Italy.
    Luperto, Matteo
    University of Milan, Milan, Italy.
    Banfi, Jacopo
    University of Milan, Milan, Italy.
    Basilico, Nicola
    University of Milan, Milan, Italy.
    Borghese, Nunzio A.
    University of Milan, Milan, Italy.
    Sioutis, Michael
    Örebro University, School of Science and Technology.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Seeking Prevention of Cognitive Decline in Elders via Activity Suggestion by A Virtual Caregiver2018In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '18), Association for Computing Machinery (ACM), 2018, p. 1835-1837Conference paper (Refereed)
    Abstract [en]

    Addressing the lack of social, cognitive, and physical stimuli among elders is a key factor to contrast Mild Cognitive Impairment (MCI) that can arise during the third age. Against such background, agent-based technology has been applied to different application domains related to the assistance of elders. In this demo, we introduce an application of this kind: an activity center featuring social, cognitive, and physical activities targeted for elders. This activity center interacts with an autonomous agent, called Virtual Caregiver, residing in the cloud and generating interventions based on users’ data. We show how the user experience can be enriched with an adaptive configuration encouraging socialization and cognitive training.

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